A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data.

We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, sex and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia, urinary tract infection and COVID-19. Heart failure was used as an objective to prove method generalizability. Chronological age was predicted by a deep neural network with R ML applied to hematology data could predict communicable and noncommunicable diseases, both at local and global levels.

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© 2021 The authors.

Déclaration de conflit d'intérêts

Financial & competing interests disclosure D Steele and C McKenzie are employees of Sysmex Corporation. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.